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Due to the complex semantics of natural language, the multi-sentiment polarity of words, and the long-dependence of sentiments between words, the existing sentiment analysis methods (especially Chinese textual sentiment analysis) still face severe challenges. Aware of these issues, this paper proposes a scalable multi-channel dilated joint architecture of convolutional neural network and bidirectional long short-term memory (CNN–BiLSTM) model with an attention mechanism to analyze the sentiment tendency of Chinese texts. Through the multi-channel structure, this model can extract both the original context features and the multiscale high-level context features. Importantly, the number of the model channel can be optimally expanded according to the actual corpus. Furthermore, the attention mechanism including local attention and global attention is adopted to further distinguish the difference of features …